In addition to CFGs, we evaluate all of our stack-augmented models on the Penn TreeBank language modeling task [1]}.
All models trained on this dataset consisted of 100 hidden units and were trained over 50 epochs using the Adam optimizer (also with patience scheduling for the learning rate). Dataset splits and settings was chosen based on prior related work [2]}. Our models were able to match the performance of the LSTM and SRCN [2]}.
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